Trying to find useful things to do with emerging technologies in open education

A Map of My Twitter Follower Network

Twitter may lay claim to millions of users, but we intend to only inhabit a small part of it… I Follow 500 or so people, and am followed by 3000 or so “curated” followers (I block maybe 20-50 a week, and not all of them obvious spam accounts, in part because I see the list of folk who follow me as a network that defines several particular spheres of interest, and I don’t want to drown out signal by noise.)

So here’s a map of how the connected component of the graph of how my Twitter followers follow each other; it excludes people who aren’t followed by anyone in the graph (which may include folk who do follow me but who have private accounts).

The layout is done in Gephi using the Force Atlas 2 layout. It’s quite by chance that the layout resembles the Isle of Wight…or a heart? Yes, maybe it’s a great be heart:-)

By running the HITS statistic over the graph, we can get a feel for who the influential folk are; sizing and labeling nodes by Authority, we get this (click through to see a bigger version):

Here’s an annotated version, as I see it (click through to see a bigger version):

If you’d like me to spend up to 20 mins making a map for you, I’ll pick up on an idea from Martin Hawksey and maybe do two or three maps for a donation to charity (in particular, to Ovacome). In fact, if you feel as if you’ve ever benefited from anything posted to this blog, why not give them a donation anyway…? Donate to ovacome.

happy to make a donation to ovacome if you would do a map for me -well actually for CETIS, Would like to see if we can make sense of (any) links between our corporate “jisccetis” twitter account and our individual ones.

I also had a (what turned out not to be so quick a) look at how @jisccetis is situated in “list space”. http://www.flickr.com/photos/psychemedia/5845175602/
That image is generated by grabbing the lists that @jisccetis is on, collecting the names of users on those lists (the nodes represent the set of folk on those lists), then graphing edges to show connections between people who are on the same list. People only on one list are collected together in well defined “list groupings”. People in the middle of the chart are members of several of the same lists that @jisccetis is. To the extent that folk independently curate lists and signal some sort of similarity between people on the same list, we see who community list curators liken to @jisccetis on the basis of joint list membership. The edges are undirected.

Here’s another example, this time of a bipartite graph, in which I plot two sorts of nodes: lists and people on lists. Edges are directed from people to the lists they are on (it really should be the other way…). This view differs from the previous one in that in this case we can see central list identifier that connects out to people on the list. http://www.flickr.com/photos/psychemedia/5844659573/
However, in terms of information it’s not so different to the previous image.

I haven’t done any analysis of the extent to which members of the lists follow each other. If you want me to do add that code and do a view, I’ll get the OU to invoice you for a day’s work;-)

This is great -thank you so much. I’ll take a closer look and comment on your other post about this. I’ll probably also blog a bit more about how we manage the @jisccetis account and try and contextualize things a bit more. Let me know your daily rate and once we have our funding confirmed I’ll see what I can do about that day invoice:-)

[…] around data visualisation. Last week he blogged about some work he had been doing visualising his twitter network, and at the end of his post offered to "spend 20 minutes or so" creating visualisations for others […]

[…] statistic and consequently group colour; this often defines different sorts of interest groups. (My follower network shows distinct groups of people from the Open University, and JISC, the HE library and educational […]

[…] Michael’s review states that no detailed analysis has been undertaken in this area as yet, however a quick look at UKCLE’s Mentionmap reveals some interesting connections. Tools to further analyse the community around the account range from FriendorFollow to Martin Hawksey’s Export Twitter followers Google spreadsheet or a full visualisation – see Tony Hirst’s Twitter follower map. […]